Hybrid Harmony Search Differential Evolution Algorithm

Zhu, Hongtao; Zhang, Chengyun; Ouyang, Haibin; Li, Steven

doi:10.1109/access.2021.3055530

Cited by 14 publications

(10 citation statements)

References 49 publications

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“…The set of benchmarks with 14 shifted-rotated CEC2005 functions with 30 variables are tested in this section. The results of CO are compared with several powerful, well-known evolutionary algorithms such as whale optimization algorithm (WOA) 44 , emperor penguin optimizer (EPO) 64 , slime mould algorithm (SMA) 65 , Jaya 66 , heat transfer search (HTS) 67 , modified particle swarm optimizer (MPSO) 68 , self-adaptive DE (jDE) 69 , DE 70 , and global and local real-coded genetic algorithms based on parent-centric crossover operators (GL-25) 71 . The parameters of each algorithm are reported in Table 4 and for a fair comparison the number of fitness evaluations are set to 3 × 10 5 for all algorithms.…”

Section: Simulation Resultsmentioning

confidence: 99%

The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems

Akbari

Zare

Azizipanah‐Abarghooee

et al. 2022

Sci Rep

127

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Motivated by the hunting strategies of cheetahs, this paper proposes a nature-inspired algorithm called the cheetah optimizer (CO). Cheetahs generally utilize three main strategies for hunting prey, i.e., searching, sitting-and-waiting, and attacking. These strategies are adopted in this work. Additionally, the leave the pray and go back home strategy is also incorporated in the hunting process to improve the proposed framework's population diversification, convergence performance, and robustness. We perform intensive testing over 14 shifted-rotated CEC-2005 benchmark functions to evaluate the performance of the proposed CO in comparison to state-of-the-art algorithms. Moreover, to test the power of the proposed CO algorithm over large-scale optimization problems, the CEC2010 and the CEC2013 benchmarks are considered. The proposed algorithm is also tested in solving one of the well-known and complex engineering problems, i.e., the economic load dispatch problem. For all considered problems, the results are shown to outperform those obtained using other conventional and improved algorithms. The simulation results demonstrate that the CO algorithm can successfully solve large-scale and challenging optimization problems and offers a significant advantage over different standards and improved and hybrid existing algorithms. Note that the source code of the CO algorithm is publicly available at https://www.optim-app.com/projects/co.

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Section: Simulation Resultsmentioning

confidence: 99%

The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems

Akbari

Zare

Azizipanah‐Abarghooee

et al. 2022

Sci Rep

127

View full text Add to dashboard Cite

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“…The differential evolution algorithm (DE) mainly consists of three processes, namely variation, crossover, and selection [ 28 , 29 ]. Its control parameters are three, which are the population size, the differential variation parameter F , and the crossover probability CR.…”

Section: Whale Optimization Algorithm Incorporates Multiple Improveme...mentioning

confidence: 99%

Multistrategy Improved Whale Optimization Algorithm and Its Application

Liu

Zhang

2022

Computational Intelligence and Neuroscience

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To address the shortcomings of the whale optimization algorithm (WOA) in terms of insufficient global search ability and slow convergence speed, a differential evolution chaotic whale optimization algorithm (DECWOA) is proposed in this paper. Firstly, the initial population is generated by introducing the Sine chaos theory at the beginning of the algorithm to increase the population diversity. Secondly, new adaptive inertia weights are introduced into the individual whale position update formula to lay the foundation for the global search and improve the optimization performance of the algorithm. Finally, the differential variance algorithm is fused to improve the global search speed and accuracy of the whale optimization algorithm. The impact of various improvement strategies on the performance of the algorithm is analyzed using different kinds of test functions that are randomly selected. The particle swarm optimization algorithm (PSO), butterfly optimization algorithm (BOA), WOA, chaotic feedback adaptive whale optimization algorithm (CFAWOA), and DECWOA algorithm are compared for the optimal search performance. Experimental simulations are performed using MATLAB software, and the results show that the improved whale optimization algorithm has a better global optimization-seeking capability. The improved whale optimization algorithm is applied to the distribution network fault location of IEEE-33 nodes, and the effectiveness and accuracy of the distribution network fault zone location based on the multistrategy improved whale optimization algorithm is verified.

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“…GWO is used to control PAR and bw parameters during the search, and OBL enhances the improvisation step. Fu et al [184] homogeneous and heterogeneous azeotropes [260,261], critical transitions [262], and bubble and dew points [263,264]. For instance, the azeotropes occur in a boiling mixture with one or two liquid phases when the composition of the vapor is the same as the overall composition of the liquid phase(s).…”

Section: Sadollah Et Al [181]mentioning

confidence: 99%

An Overview of the Application of Harmony Search for Chemical Engineering Optimization

Fonseca-Pérez

Del-Mazo-Alvarado

Luna

et al. 2022

International Journal of Chemical Engineering

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Harmony search algorithm and its variants have been used in several applications in medicine, telecommunications, computer science, and engineering. This article reviews the global and multi-objective optimization for chemical engineering using harmony search. The main features of the HS method and several of its popular variants and hybrid versions including their relevant algorithm characteristics are described and discussed. A variety of global and multi-objective optimization problems from chemical engineering and their resolution using HS-based methods are also included. These problems involve thermodynamic calculations (phase stability analysis, phase equilibrium calculations, parameter estimation, and azeotrope calculation), heat exchanger design, distillation simulation, life cycle analysis, and water distribution systems, among others. Remarks on future developments of HS and its related algorithms for global and multi-objective optimization in chemical engineering are also provided in this review. HS is a reliable and promising stochastic optimizer to resolve challenging global and multi-objective optimization problems for process systems engineering.

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Hybrid Harmony Search Differential Evolution Algorithm

Cited by 14 publications

References 49 publications

The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems

The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems

Multistrategy Improved Whale Optimization Algorithm and Its Application

An Overview of the Application of Harmony Search for Chemical Engineering Optimization

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